The binning specification is taken to use the Automatic underlying binning method.

Possible named binning methods include:

"Sturges"

compute the number of bins based on the length of data

"Scott"

asymptotically minimize the mean square error

"FreedmanDiaconis"

twice the interquartile range divided by the cube root of sample size

"Knuth"

balance likelihood and prior probability of a piecewise uniform model

"Wand"

one-level recursive approximate Wand binning

The function fb in PairedHistogram[data1,data2,fb] is applied to a list of all and and should return an explicit bin list .

Different forms of histogram can be obtained by giving different bin height specifications hspec in PairedHistogram[data1,data2,bspec,hspec]. The following forms can be used:

"Count"

number of elements in each bin

"CumulativeCount"

cumulative counts

"SurvivalCount"

survival counts

"Probability"

fraction of values lying in each bin

"Intensity"

count divided by bin width

"PDF"

probability density function

"CDF"

cumulative distribution function

"SF"

survival function

"HF"

hazard function

"CHF"

cumulative hazard function

{"Log",hspec}

log transformed height specification

fh

heights obtained by applying fh to bins and counts

The function fh in PairedHistogram[data1,data2,bspec,fh] is applied to two arguments: a list of bins and corresponding list of counts . The function should return a list of heights to be used for each of the .

Only values that are real numbers are assigned to bins; others are taken to be missing.

In PairedHistogram[{data11,…},{data21,…},…], automatic bin locations are determined by combining all the datasets and .

PairedHistogram[{…,wi[datai,…],…},{…,wj[dataj],…},…] renders the histogram elements associated with dataset according to the specification defined by the symbolic wrapper .